| Title |
Design of Hybrid Magnetic Levitation System using Intellignet Optimization Algorithm |
| Authors |
조재훈(Cho, Jae-Hoon) ; 김용태(Kim, Yong-Tae) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.12.1782 |
| Keywords |
Intelligent optimization ; Hybrid magnetic levitation system ; Teaching-learning based optimization ; Magnetic equivalent circuit |
| Abstract |
In this paper, an optimal design of hybrid magnetic levitation(Maglev) system using intelligent optimization algorithms is proposed. The proposed maglev system adopts hybrid suspension system with permanent-magnet(PM) and electro magnet(EM) to reduce the suspension power loss and the teaching-learning based optimization(TLBO) that can overcome the drawbacks of conventional intelligent optimization algorithm is used. To obtain the mathematical model of hybrid suspension system, the magnetic equivalent circuit including leakage fluxes are used. Also, design restrictions such as cross section areas of PM and EM, the maximum length of PM, magnetic force are considered to choose the optimal parameters by intelligent optimization algorithm. To meet desired suspension power and lower power loss, the multi object function is proposed. To verify the proposed object function and intelligent optimization algorithms, we analyze the performance using the mean value and standard error of 10 simulation results. The simulation results show that the proposed method is more effective than conventional optimization methods. |